Here's how you can ensure your data visualizations meet end user needs and expectations.

Na minha vis?o, entender quem s?o os usuários finais é fundamental. Identifique o nível de conhecimento técnico e familiaridade com os dados. Pergunte-se: s?o analistas de dados, executivos de negócios, ou o público em geral? Compreender a audiência ajuda a adaptar a complexidade e o estilo das visualiza??es para garantir clareza e relevancia.

Effective data visualization hinges on understanding your audience. Consider their background, expertise, and objectives when crafting visuals. Data scientists may favor intricate charts for in-depth analysis, while marketing teams may require simplified, high-level summaries. Tailor your designs accordingly to ensure they resonate with your viewers, making the information accessible and relevant to their needs. This approach enhances engagement and facilitates clearer communication of insights through data visualization techniques.

Understanding your audience is essential for crafting effective data visualizations. Consider their background, expertise, and what they need from the data. For instance, data scientists might prefer complex, detailed visuals for deep analysis, while marketing teams often need simpler, high-level summaries. Tailor your visualizations to match their knowledge level and interests. This means selecting appropriate chart types and ensuring clarity in presentation. By aligning your design with the audience’s needs, you make the data more accessible and impactful, allowing users to quickly and effectively engage with and understand the insights you are presenting.

It's important to know who you are presenting to. Different audiences with different metrics and problems. The visualisation solution should be tailored toward specific problems.

Here's the key: Design for the user. Understand their needs: Research user background, goals, and data literacy. Keep it clear and concise: Focus on the message, avoid clutter, and use clear labels. Choose the right chart type: Match the chart to the data and user's task (e.g., compare trends vs. show composition). Test and iterate: Get user feedback and refine the visualization based on their input.

In my opinion, clearly defining the goals of your visualization is essential. Determine what you want to achieve with the visualization – whether it's to inform, persuade, or explore. Having clear objectives will guide your design choices and ensure that the visualization effectively communicates the intended message.

One important point I have found useful is to know "very clearly" what decisions/ insights can be obtained from this data. Framing the goals to drive decisions is the key when we are in a business context. Working backwards helps to decide on data points and visualization required for it

Clearly defining the goals of your data visualization is fundamental to its success. First, determine what you want your audience to understand or do after seeing the visualization. Are you aiming to reveal trends, compare different data points, or illustrate a company’s quarterly growth? By establishing these specific objectives, you can choose the most appropriate type of visualization and design it to guide your audience towards the desired insights. This approach ensures your visualization is not only visually engaging but also functional and informative, allowing users to easily comprehend the key messages and make informed decisions based on the data presented.

Your goal is the climax of your narrative. It's crucial to include tradeoffs and clearly establish what you're presenting—is it a trend, an anomaly, revenue, or losses? Determine how you want to influence your audience to believe and understand the story your data tells.

To ensure your data visualizations meet end user needs, start by defining clear goals. Pinpoint what your audience should understand or do after viewing the visualization. Determine if the aim is to identify trends, compare data points, or communicate growth. By setting specific objectives, you can select the appropriate type of visualization and design it to guide your audience to the intended conclusions. This ensures the visualization is effective and informative.

Selecting the right type of visualization is crucial. For example, pie charts effectively show parts of a whole, bar graphs compare quantities, line plots illustrate trends over time, and heat maps reveal patterns across variables. Matching the visualization type to the data and the story you want to tell ensures clarity. This approach makes the data intuitive and accessible to the user, meeting their needs and expectations effectively.

Más allá de seleccionar el tipo correcto de visualización, es fundamental considerar la interactividad y personalización que herramientas como Tableau pueden ofrecer. Proporcionar a la audiencia la capacidad de profundizar en los datos, filtrar información específica y ver detalles bajo demanda puede transformar una visualización estática en una experiencia dinámica y enriquecedora. Esto no solo facilita una comprensión más profunda, sino que también permite a los usuarios descubrir insights por sí mismos, adaptando la visualización a sus propias preguntas y necesidades. La elección del tipo correcto de visualización es el primer paso, pero habilitar la interactividad lleva la efectividad a un nivel superior.

Nature of data: Step 1: dentify what kind of data, whether it belongs to the categorical, numerical, temporal or geographical type. Choose the visualization the best represent the data and provide key insights and highlights important points. For eg: use bar charts for categorical comparisons, Line charts for trend over time, Scatter plots for relationship between variables, Maps for geographical data, Pie charts for parts of a whole, Histograms for distributions and Heatmaps for density and correlation.

Select the most appropriate chart or graph type (e.g., bar charts for comparisons, line charts for trends, pie charts for proportions). Ensure the chosen type effectively communicates the intended message

Use clear, straightforward visuals that are easy to understand at a glance. Avoid clutter and unnecessary details. Choose the most appropriate chart types for your data and audience. Highlight key insights and make the data interactive if possible. By keeping the design simple, you make it easier for end users to grasp the information quickly and accurately, meeting their needs effectively.

Focus on making data visualization simple and accessible. Use clear, legible fonts and high-contrast colors to make sure everyone in the audience can understand the information. Highlight the most important data and use straightforward charts or graphs to avoid clutter. Easy-to-follow labels, legends, and alternative text for descriptions are other important design principles.

Your audience need not be always appreciative of Visualisation. Your intent and focus while preparing a visualisation should be to ensure your audience understand. The mantra is simplicity.

- Don't put too many statistics in one single visualisation. - The key focus should be to show most important statistics at first glance. - Don't use reference levels that might confuse the viewer in picking up insight. - Avoid using multiple axis for same type of plots - Speak up in units that are more common rather than pure whole numbers. - Use colour variations wisely, don't use too many colours. The variations might be used in tone contrasts to show variations for same kind of data comparisions.

One thing I’ve found helpful is having layers of data. The first layer showcases high level overview of numbers. Second layer is more granular than the first and the third will have more granularity and details than the second and so on. Key is to (i) identify different stakeholders in your audience, (ii) what level of granularity makes sense for them, (iii) identify the number of layers you would need and lastly (iv) define the level of detail on each layer.

In my experience, iterating based on feedback is vital for creating effective visualizations. Share your drafts with a small group of end users and gather their input. Listen to their suggestions and make necessary adjustments. This collaborative approach ensures that the final visualization meets user needs and expectations.

Prototype the visualization and gather feedback from a sample of end users. Use this feedback to make necessary adjustments and improvements. Iterate on the design until it effectively meets users’ needs.

Start by seeking and incorporating feedback through the design process. Engage with users early to understand their requirements and preferences. Use this feedback to make improvements, making sure each version better aligns with needs. Be clear and concise for comprehension and engagement. Prioritize simplicity and relevance, avoiding unnecessary complexity. Regularly validate visualizations to confirm they communicate the intended insights, fostering a collaborative and user-centered approach.

Gather User Input: Present draft visualizations to a sample of your intended audience and collect feedback. Understand if they are interpreting the data correctly and identify any points of confusion. Refine and Improve: Use feedback to iterate on your visualizations, making adjustments that improve usability and alignment with user expectations. This iterative process ensures that the final visualizations meet user needs effectively.

The feed back loop is important to ensure you optimise your visuals for the end audience. Plot alternative visuals see which ones they prefer and build on feedback

Testing your visualization dashboard is one key step before sharing it to the audience. It will ensure that the desired objective is being achieved, various charts are interactive and explanatory. Testing will also help in checking if the data is presented in a manner which does not mislead the user and gives any incorrect information. Try to test it out with a user who is not familiar with the data and understand about the experience and issues.

While thorough technical testing is crucial, there's another way to ensure your data visualizations meet user needs: "The Friend Test". Here's how it works: Prepare your visualization but don't provide any background information. Show it to a friend/colleague who isn't familiar with the data or your goals. Ask them to describe what they see, where their attention goes, what questions they have. By seeing the visualization with "fresh eyes," you identify areas that might be unclear to your actual audience. You can assess if your intended message is coming across effectively or if the visualization leads to unintended interpretations. Based on their feedback, you can refine the visualization to ensure it clearly communicates what you want.

Además de las pruebas exhaustivas, es crucial considerar la experiencia del usuario final y la accesibilidad. Asegúrate de que tu visualización no solo sea precisa y funcional, sino también intuitiva y fácil de navegar. Realiza pruebas de usabilidad con usuarios reales y recolecta sus opiniones sobre la fluidez de la interacción y la claridad de los datos presentados. Herramientas como Tableau permiten ajustes específicos para mejorar la experiencia en distintos dispositivos. Incorporar estas pruebas adicionales te ayudará a ofrecer una visualización de datos que no solo sea técnicamente sólida, sino también altamente efectiva y agradable para todos los usuarios.

Data could be a double-sword. As management is using the data for decision making. Without accuracy being instill, misleading perception could lead to wrong decision making and imposing company at risk.

Ensure Accuracy and Functionality: Before finalizing, thoroughly test your visualizations for accuracy and functionality across different devices and platforms. Ensure interactive features work as intended and that the data presented is reliable. User Experience Testing: Test the user experience to confirm that the visualization is intuitive and provides a seamless journey from data exploration to insights.

7. Add a component of Dynamicity If possible make your visualization/ dashboard dynamic i.e. enable functionality that can take user inputs and simultaneously gives out real time output for the user to form conclusions. This makes the visualization comprehensive, thus making it more useful for the end user. One example of the same could be sensitivity analysis or visualization of scenario based outcomes which are not discrete and require the user inputs (basically where all scenarios can not be pre forecasted).

I think the previous 6 points cover everything what I might add is "Go with the flow" you don't need your personal "super" visualizations, see the current trends in the field and adapt it to your case with emphasis to simplicity and effectiveness

Don’t do Data dump. More data points in the dashboard doesn’t mean it will be appreciated more by the potential consumer. More data visualisation sometimes lead to higher confusion on the insight that we’d like to provide. Instead, curate carefully on what matters to our customer persona. Tailor what they like to see first. Data consumption must tally with our expected persona’s maturity level. Additional data visualization can always be added later. Let their maturity on data grow together with our dashboard.

Understand the theme colours of the client for effective visualisations. This goes hand in hand with the norms, values, and beliefs of the intended audience. This is crucial as colours portray different meanings in different cultures. As a result, the visual can effectively tell the story!

Além dos temas acima tratados, é necessário treinar o usuário. N?o espere que o RH ensine seu cliente interno sobre o que é um Dashboard. Agende reuni?es de treinamento, pegue na m?o e ensine a fazer as análises conforme você projetou para que fossem feitas. Um cliente que n?o sabe analisar os dados da melhor forma possível n?o conseguirá explicar o que precisa melhorar e nem ajudar na constru??o de novos relatórios. Esse é o mesmo cliente tem potencial para abandonar o seu dashboard na primeira ou segunda dificuldade.